MembraneLogic - A database to couple membrane protein sequences to variation and disease

Bioinformatics data is growing faster than exponentially. This large amount of data has
opened up many possibilities but it also poses many challenges as it is a tedious task to
manually analyze this data.
25% of our genes translates to membrane proteins and they contain 60% of the drug
targets. A genetic variation can alter the function of a protein, this can sometimes lead to a
disease. Knowing where in the protein structure a disease causing variation occurs will aid
drug designers in the drug discovery process.
MembraneLogic is a database that through its automated data gathering process identifies
the membrane proteins by prediction and links them to known SNPs and their relation to
disease.
The data it uses comes from NCBI Ensembl and PDB and the main prediction method
used to identify membrane proteins is SCAMPI.
Its web interface provides functionality to search for membrane proteins and diseases and
see what variations are related to disease ,both in sequence and structure.
The application is implemented with JAVA platform related tools and frameworks such as
Grails, Spring and Hibernate.

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BibTeX @mastersthesis{Pouya2011,author={Pouya, Iman},title={MembraneLogic - A database to couple membrane protein sequences to variation and disease},abstract={Bioinformatics data is growing faster than exponentially. This large amount of data has
opened up many possibilities but it also poses many challenges as it is a tedious task to
manually analyze this data.<br>
25% of our genes translates to membrane proteins and they contain 60% of the drug
targets. A genetic variation can alter the function of a protein, this can sometimes lead to a
disease. Knowing where in the protein structure a disease causing variation occurs will aid
drug designers in the drug discovery process.<br>
MembraneLogic is a database that through its automated data gathering process identifies
the membrane proteins by prediction and links them to known SNPs and their relation to
disease.<br>
The data it uses comes from NCBI Ensembl and PDB and the main prediction method
used to identify membrane proteins is SCAMPI.<br>
Its web interface provides functionality to search for membrane proteins and diseases and
see what variations are related to disease ,both in sequence and structure.
The application is implemented with JAVA platform related tools and frameworks such as
Grails, Spring and Hibernate.},publisher={Institutionen för data- och informationsteknik, Datavetenskap (Chalmers), Chalmers tekniska högskola},place={Göteborg},year={2011},note={35},}

RefWorks RT GenericSR ElectronicID 138838A1 Pouya, ImanT1 MembraneLogic - A database to couple membrane protein sequences to variation and diseaseYR 2011AB Bioinformatics data is growing faster than exponentially. This large amount of data has
opened up many possibilities but it also poses many challenges as it is a tedious task to
manually analyze this data.<br>
25% of our genes translates to membrane proteins and they contain 60% of the drug
targets. A genetic variation can alter the function of a protein, this can sometimes lead to a
disease. Knowing where in the protein structure a disease causing variation occurs will aid
drug designers in the drug discovery process.<br>
MembraneLogic is a database that through its automated data gathering process identifies
the membrane proteins by prediction and links them to known SNPs and their relation to
disease.<br>
The data it uses comes from NCBI Ensembl and PDB and the main prediction method
used to identify membrane proteins is SCAMPI.<br>
Its web interface provides functionality to search for membrane proteins and diseases and
see what variations are related to disease ,both in sequence and structure.
The application is implemented with JAVA platform related tools and frameworks such as
Grails, Spring and Hibernate.PB Institutionen för data- och informationsteknik, Datavetenskap (Chalmers), Chalmers tekniska högskola,LA engLK http://publications.lib.chalmers.se/records/fulltext/138838.pdfOL 30